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Heat pipe is an effective device for heat transferring. Using nanofluid as working fluid can significantly increase heat pipe thermal performance. But rate of the performance improvement, is dependent on parameters of the suspended nanoparticles in nanofluid. In this article, for the first time by considering nanoparticle volume fractions and diameters as design variables and the difference between the wall temperature of evaporator and condenser and liquid pressure drop as objective functions, the heat pipe performance has optimized. The used heat pipe is a cylindrical heat pipe with nanofluid as working fluid. Heat pipe thermal performance while using nanofluid has modeled by CFD method and then GEvoM has used to relate between design variables and objective functions. Using the modified NSGAII approach, pareto front has plotted and the values of recommended optimum points has obtained by mapping method. Recommended design points unveil interesting and important optimal design principles that would not have been obtained without the use of a multi-objective optimization approach.

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Application of either protein or carbohydrate-based products as fat replacers in low fat ice-creams can improve the properties of these products. However, the type and level of fat and fat replacer utilized are affected by such different parameters as functional ones, namely: viscosity and overrun, hardness and melting rate, nutritional properties (calories) as well as the price of the final product. Throughout the present study, single- and multi-objective optimization method as based on the genetic algorithms (GAs) was applied to select the suitable fat-free as well as low-fat ice-cream formulations. The data related to single-objective optimization of selected parameters revealed that the ice-creams containing 3.5% Simplesse plus 1.72% fat, and 2.95% Maltodextrin plus 1.87% fat have ended up with the most desirable functional objectives. The application of multi-objective optimization led to a range of solutions of different fat and fat replacer contents out of which the producers can adopt the most suitable choice depending on the needs.

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In this study, performance of gas liquid cylindrical cyclone separators and the effect of changing geometrical parameters of cyclone separators entrance is investigated. The cyclone is simulated with computational fluid dynamic methods. After choosing a suitable mesh grid for the cyclone and checking grid independency, the effect of changing entrance geometry on gas carry under and liquid carry over is investigated. Geometrical parameters, especially inlet geometrical parameters have great effect on optimizing cyclone separators performance. RSM model is used for turbulence simulation of the flow and two phase flow is simulated using Eulerian- Eulerian approach. In this simulation, inlet cross section, inlet angle and inlet height relative to the cyclone bottom part are optimized. Results show that GCU decreases with decreasing nozzle’s inlet angle. An optimum point for GCU was given with changing inlet altitude relative to the bottom of the cyclone and inlet nozzle’s width. An optimum point for LCO was obtained with changing inlet altitude and inlet nozzle width. Increasing inlet angle causes a decrease in LCO. In optimum model, gas carry under decreases significantly and liquid carry over is eliminated.

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In this paper, modeling of Min-Max controller and evolutionary multiobjective optimization for gain tuning controller of turbofan engine are presented. To achieve this purpose, first a turbofan engine is modeledin GSP software. Then engine parameters model, by using extracted GSP simulation data and based onNARX structure of neural network is developed. For model validation a test fuel signal is produced and model performance is assessed by means of it. Next, turbofan engines control requirements and constraints are described and in accordance with it a fuel controller based on Min-Max strategy is designed and diverse control loops in controller is described. Each of theseloopshas aproportionalcontroller that are knownascontrol gains of the min-max controller. Then, for determining the gains of the controller, gain tuning process is formulated as a Genetic Algorithm Optimization problem in order to GA algorithm finds the best solution by its evolutionary generations. In this optimization problem, the settling time during acceleration and deceleration, engine fuel consumption and the amount of engine emission are considered as objective functions to be minimized. The obtained results from simulation of optimized controller and engine show, the final controller not only optimizes objective functions but also satisfies all control modes of engine during acceleration and deceleration modes.

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In today’s world, using of biogas is increasing due to its methane content, renewability, and low price. Solid oxide fuel cell is one of the best energy conversion technologies, in order to use biogas and it has a high potential to integrate with the gas turbine. In this paper, solid oxide fuel cell-gas turbine hybrid system, which is fed by biogas is modeled with respect to energy and economic aspects. Maximization of electrical energy efficiency and minimization of total investment cost are objective functions, which are considered to find the optimal design variables of the hybrid system. First, each component of the hybrid system is modeled and validated individually. Then, in order to optimize the hybrid system, multi objective optimization via NSGAII is implemented and optimal values of design parameters of the hybrid system were calculated. Optimal point is obtained using Euclidian non-dimensionalization and LINMAP decision making method in Pareto front. So, optimal design values are 66 percent and 175227.4 $, which are electrical energy efficiency and total investment cost, respectively. In optimal point Levelized unit cost is 6.3 cent per kWh. Finally, in order to determine the effect of design parameters on the objective functions, sensitivity of each design parameters were analyzed using Sobol's sensitivity analysis method. Results show that compressor pressure ratio has the maximum effect on electrical energy efficiency. Furthermore, turbine isentropic efficiency and fuel cell current have the maximum effect on the total investment cost.

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Nowadays, two-layer sheets have many applications in various industries due to their superlative characteristics. Characteristics such as weight and formability of two-layer sheet depend on the material and the thickness of the layers which compose the two-layer sheet. Plastic instability and occurrence of localized necking limit the forming of the sheets. Forming limit diagram is used to evaluate the formability of sheet. In this paper, a multi-objective genetic algorithm is applied to optimize the thickness ratio of layers in Al3105-St14 two-layer sheet. The optimal model minimizes the weight and maximizes the formability of two-layer sheet simultaneously. Forming limit diagram of two-layer sheet is determined by analytical model based on Marciniak and Kuckzinsky (M-K) method using Barlat and Lian non-quadratic yield criterion. Experiments are also carried out on Al3105-St14 two-layer sheet in order to examine the validity of the theoretical results. Pareto-based multi-objective optimization is used in order to make the objective function of weight per unit area minimized and the objective function of formability maximized. The Pareto front provides a set of optimal solutions. In addition, the knee point as the most satisfactory solution from Pareto-set is determined using minimum distance method.